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20172023
most citedQuantum State Preparation with Optimal Circuit Depth: Implementations and Applications

171 citations · 480 across the 25 of their papers we have counts for

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Showing 2023 · quant-phShow all

7 papers · 2 filters

quant-ph2023★ 12 cited

Unleashing the Potential of LLMs for Quantum Computing: A Study in Quantum Architecture Design

Zhiding Liang, Jinglei Cheng, Rui Yang +6

Large Language Models (LLMs) contribute significantly to the development of conversational AI and has great potentials to assist the scientific research in various areas. This pape…

quant-ph2023★ 3 cited

Exponential speedups for quantum walks in random hierarchical graphs

Shankar Balasubramanian, Tongyang Li, Aram Harrow

There are few known exponential speedups for quantum algorithms and these tend to fall into even fewer families. One speedup that has mostly resisted generalization is the use of q…

quant-ph2023

Near-Optimal Quantum Coreset Construction Algorithms for Clustering

Yecheng Xue, Xiaoyu Chen, Tongyang Li +1

-Clustering in (e.g., -median and -means) is a fundamental machine learning problem. While near-linear time approximation algorithms were known in the class…

quant-ph2023

Fidelity estimator, randomized benchmarking and ZNE for quantum pulses

Jinglei Cheng, Zhiding Liang, Rui Yang +4

Most previous research focused on designing pulse programs without considering the performance of individual elements or the final fidelity. To evaluate the performance of quantum…

quant-ph2023★ 3 cited

Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games

Minbo Gao, Zhengfeng Ji, Tongyang Li +1

We propose the first online quantum algorithm for solving zero-sum games with regret under the game setting. Moreover, our quantum algorithm computes an $\varepsi…

quant-ph2023★ 1 cited

Towards Advantages of Parameterized Quantum Pulses

Zhiding Liang, Jinglei Cheng, Zhixin Song +7

The advantages of quantum pulses over quantum gates have attracted increasing attention from researchers. Quantum pulses offer benefits such as flexibility, high fidelity, scalabil…

quant-ph2023★ 1 cited

Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret

Han Zhong, Jiachen Hu, Yecheng Xue +2

While quantum reinforcement learning (RL) has attracted a surge of attention recently, its theoretical understanding is limited. In particular, it remains elusive how to design pro…